Overview

Dataset statistics

Number of variables25
Number of observations30,000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory130.1 B

Variable types

Numeric21
Categorical4

Alerts

payment_status_sep is highly overall correlated with payment_status_aug and 2 other fieldsHigh correlation
payment_status_aug is highly overall correlated with payment_status_sep and 7 other fieldsHigh correlation
payment_status_jul is highly overall correlated with payment_status_sep and 9 other fieldsHigh correlation
payment_status_jun is highly overall correlated with payment_status_sep and 10 other fieldsHigh correlation
payment_status_may is highly overall correlated with payment_status_aug and 8 other fieldsHigh correlation
payment_status_apr is highly overall correlated with payment_status_aug and 8 other fieldsHigh correlation
bill_statement_sep is highly overall correlated with payment_status_aug and 8 other fieldsHigh correlation
bill_statement_aug is highly overall correlated with payment_status_aug and 10 other fieldsHigh correlation
bill_statement_jul is highly overall correlated with payment_status_aug and 11 other fieldsHigh correlation
bill_statement_jun is highly overall correlated with payment_status_jul and 13 other fieldsHigh correlation
bill_statement_may is highly overall correlated with payment_status_jul and 13 other fieldsHigh correlation
bill_statement_apr is highly overall correlated with payment_status_jun and 11 other fieldsHigh correlation
previous_payment_sep is highly overall correlated with bill_statement_sep and 5 other fieldsHigh correlation
previous_payment_aug is highly overall correlated with bill_statement_jul and 5 other fieldsHigh correlation
previous_payment_jul is highly overall correlated with bill_statement_jun and 7 other fieldsHigh correlation
previous_payment_jun is highly overall correlated with bill_statement_jun and 6 other fieldsHigh correlation
previous_payment_may is highly overall correlated with bill_statement_jun and 5 other fieldsHigh correlation
previous_payment_apr is highly overall correlated with bill_statement_may and 4 other fieldsHigh correlation
previous_payment_aug is highly skewed (γ1 = 30.45381745)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
payment_status_sep has 14737 (49.1%) zerosZeros
payment_status_aug has 15730 (52.4%) zerosZeros
payment_status_jul has 15764 (52.5%) zerosZeros
payment_status_jun has 16455 (54.9%) zerosZeros
payment_status_may has 16947 (56.5%) zerosZeros
payment_status_apr has 16286 (54.3%) zerosZeros
bill_statement_sep has 2008 (6.7%) zerosZeros
bill_statement_aug has 2506 (8.4%) zerosZeros
bill_statement_jul has 2870 (9.6%) zerosZeros
bill_statement_jun has 3195 (10.7%) zerosZeros
bill_statement_may has 3506 (11.7%) zerosZeros
bill_statement_apr has 4020 (13.4%) zerosZeros
previous_payment_sep has 5249 (17.5%) zerosZeros
previous_payment_aug has 5396 (18.0%) zerosZeros
previous_payment_jul has 5968 (19.9%) zerosZeros
previous_payment_jun has 6408 (21.4%) zerosZeros
previous_payment_may has 6703 (22.3%) zerosZeros
previous_payment_apr has 7173 (23.9%) zerosZeros

Reproduction

Analysis started2025-08-03 22:16:00.922132
Analysis finished2025-08-03 22:17:12.302513
Duration1 minute and 11.38 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15000.5
Minimum1
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:12.402951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500.95
Q17500.75
median15000.5
Q322500.25
95-th percentile28500.05
Maximum30000
Range29999
Interquartile range (IQR)14999.5

Descriptive statistics

Standard deviation8660.3984
Coefficient of variation (CV)0.57734065
Kurtosis-1.2
Mean15000.5
Median Absolute Deviation (MAD)7500
Skewness0
Sum4.50015 × 108
Variance75002500
MonotonicityStrictly increasing
2025-08-03T15:17:12.567740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
19997 1
 
< 0.1%
20009 1
 
< 0.1%
20008 1
 
< 0.1%
20007 1
 
< 0.1%
20006 1
 
< 0.1%
20005 1
 
< 0.1%
20004 1
 
< 0.1%
20003 1
 
< 0.1%
20002 1
 
< 0.1%
Other values (29990) 29990
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
30000 1
< 0.1%
29999 1
< 0.1%
29998 1
< 0.1%
29997 1
< 0.1%
29996 1
< 0.1%
29995 1
< 0.1%
29994 1
< 0.1%
29993 1
< 0.1%
29992 1
< 0.1%
29991 1
< 0.1%

limit_bal
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:12.745872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.66
Coefficient of variation (CV)0.77468541
Kurtosis0.5362629
Mean167484.32
Median Absolute Deviation (MAD)90000
Skewness0.99286696
Sum5.0245297 × 109
Variance1.6834456 × 1010
MonotonicityNot monotonic
2025-08-03T15:17:12.915122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3365
 
11.2%
20000 1976
 
6.6%
30000 1610
 
5.4%
80000 1567
 
5.2%
200000 1528
 
5.1%
150000 1110
 
3.7%
100000 1048
 
3.5%
180000 995
 
3.3%
360000 881
 
2.9%
60000 825
 
2.8%
Other values (71) 15095
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1976
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3365
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1567
5.2%
90000 651
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
Female
18112 
Male
11888 

Length

Max length6
Median length6
Mean length5.2074667
Min length4

Characters and Unicode

Total characters156,224
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 18112
60.4%
Male 11888
39.6%

Length

2025-08-03T15:17:13.084822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T15:17:13.247685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
female 18112
60.4%
male 11888
39.6%

Most occurring characters

ValueCountFrequency (%)
e 48112
30.8%
a 30000
19.2%
l 30000
19.2%
F 18112
 
11.6%
m 18112
 
11.6%
M 11888
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126224
80.8%
Uppercase Letter 30000
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48112
38.1%
a 30000
23.8%
l 30000
23.8%
m 18112
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
F 18112
60.4%
M 11888
39.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 156224
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48112
30.8%
a 30000
19.2%
l 30000
19.2%
F 18112
 
11.6%
m 18112
 
11.6%
M 11888
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48112
30.8%
a 30000
19.2%
l 30000
19.2%
F 18112
 
11.6%
m 18112
 
11.6%
M 11888
 
7.6%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
University
14030 
Graduate school
10585 
High school
4917 
Others
 
468

Length

Max length15
Median length11
Mean length11.865667
Min length6

Characters and Unicode

Total characters355,970
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUniversity
2nd rowUniversity
3rd rowUniversity
4th rowUniversity
5th rowUniversity

Common Values

ValueCountFrequency (%)
University 14030
46.8%
Graduate school 10585
35.3%
High school 4917
 
16.4%
Others 468
 
1.6%

Length

2025-08-03T15:17:13.370395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T15:17:13.530672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
school 15502
34.1%
university 14030
30.8%
graduate 10585
23.3%
high 4917
 
10.8%
others 468
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 32977
 
9.3%
o 31004
 
8.7%
s 30000
 
8.4%
e 25083
 
7.0%
r 25083
 
7.0%
t 25083
 
7.0%
a 21170
 
5.9%
h 20887
 
5.9%
15502
 
4.4%
l 15502
 
4.4%
Other values (11) 113679
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 310468
87.2%
Uppercase Letter 30000
 
8.4%
Space Separator 15502
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 32977
10.6%
o 31004
10.0%
s 30000
9.7%
e 25083
 
8.1%
r 25083
 
8.1%
t 25083
 
8.1%
a 21170
 
6.8%
h 20887
 
6.7%
l 15502
 
5.0%
c 15502
 
5.0%
Other values (6) 68177
22.0%
Uppercase Letter
ValueCountFrequency (%)
U 14030
46.8%
G 10585
35.3%
H 4917
 
16.4%
O 468
 
1.6%
Space Separator
ValueCountFrequency (%)
15502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 340468
95.6%
Common 15502
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 32977
 
9.7%
o 31004
 
9.1%
s 30000
 
8.8%
e 25083
 
7.4%
r 25083
 
7.4%
t 25083
 
7.4%
a 21170
 
6.2%
h 20887
 
6.1%
l 15502
 
4.6%
c 15502
 
4.6%
Other values (10) 98177
28.8%
Common
ValueCountFrequency (%)
15502
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 355970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 32977
 
9.3%
o 31004
 
8.7%
s 30000
 
8.4%
e 25083
 
7.0%
r 25083
 
7.0%
t 25083
 
7.0%
a 21170
 
5.9%
h 20887
 
5.9%
15502
 
4.4%
l 15502
 
4.4%
Other values (11) 113679
31.9%

marriage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 KiB
Single
15964 
Married
13659 
Others
 
377

Length

Max length7
Median length6
Mean length6.4553
Min length6

Characters and Unicode

Total characters193,659
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Single 15964
53.2%
Married 13659
45.5%
Others 377
 
1.3%

Length

2025-08-03T15:17:13.716605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T15:17:13.852097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
single 15964
53.2%
married 13659
45.5%
others 377
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 30000
15.5%
i 29623
15.3%
r 27695
14.3%
S 15964
8.2%
n 15964
8.2%
g 15964
8.2%
l 15964
8.2%
M 13659
7.1%
a 13659
7.1%
d 13659
7.1%
Other values (4) 1508
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 163659
84.5%
Uppercase Letter 30000
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30000
18.3%
i 29623
18.1%
r 27695
16.9%
n 15964
9.8%
g 15964
9.8%
l 15964
9.8%
a 13659
8.3%
d 13659
8.3%
t 377
 
0.2%
h 377
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S 15964
53.2%
M 13659
45.5%
O 377
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 193659
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30000
15.5%
i 29623
15.3%
r 27695
14.3%
S 15964
8.2%
n 15964
8.2%
g 15964
8.2%
l 15964
8.2%
M 13659
7.1%
a 13659
7.1%
d 13659
7.1%
Other values (4) 1508
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30000
15.5%
i 29623
15.3%
r 27695
14.3%
S 15964
8.2%
n 15964
8.2%
g 15964
8.2%
l 15964
8.2%
M 13659
7.1%
a 13659
7.1%
d 13659
7.1%
Other values (4) 1508
 
0.8%

age
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:14.337687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2179041
Coefficient of variation (CV)0.25976537
Kurtosis0.044303378
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.73224587
Sum1064565
Variance84.969755
MonotonicityNot monotonic
2025-08-03T15:17:14.504347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1605
 
5.3%
27 1477
 
4.9%
28 1409
 
4.7%
30 1395
 
4.7%
26 1256
 
4.2%
31 1217
 
4.1%
25 1186
 
4.0%
34 1162
 
3.9%
32 1158
 
3.9%
33 1146
 
3.8%
Other values (46) 16989
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 931
3.1%
24 1127
3.8%
25 1186
4.0%
26 1256
4.2%
27 1477
4.9%
28 1409
4.7%
29 1605
5.3%
30 1395
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

payment_status_sep
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2025-08-03T15:17:14.640171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1238015
Coefficient of variation (CV)-67.293505
Kurtosis2.720715
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.73197493
Sum-501
Variance1.2629299
MonotonicityNot monotonic
2025-08-03T15:17:14.773745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.1%
-1 5686
 
19.0%
1 3688
 
12.3%
-2 2759
 
9.2%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2759
 
9.2%
-1 5686
 
19.0%
0 14737
49.1%
1 3688
 
12.3%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2667
 
8.9%
1 3688
 
12.3%
0 14737
49.1%
-1 5686
 
19.0%

payment_status_aug
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13376667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2025-08-03T15:17:14.892969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197186
Coefficient of variation (CV)-8.9498079
Kurtosis1.5704177
Mean-0.13376667
Median Absolute Deviation (MAD)0
Skewness0.79056502
Sum-4013
Variance1.4332543
MonotonicityNot monotonic
2025-08-03T15:17:15.037518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.4%
-1 6050
 
20.2%
2 3927
 
13.1%
-2 3782
 
12.6%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3782
 
12.6%
-1 6050
 
20.2%
0 15730
52.4%
1 28
 
0.1%
2 3927
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3927
 
13.1%
1 28
 
0.1%
0 15730
52.4%
-1 6050
 
20.2%

payment_status_jul
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2025-08-03T15:17:15.168724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1968676
Coefficient of variation (CV)-7.2013692
Kurtosis2.0844359
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.84068183
Sum-4986
Variance1.432492
MonotonicityNot monotonic
2025-08-03T15:17:15.289748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.5%
-1 5938
 
19.8%
-2 4085
 
13.6%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4085
 
13.6%
-1 5938
 
19.8%
0 15764
52.5%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 76
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.5%
-1 5938
 
19.8%

payment_status_jun
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22066667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2025-08-03T15:17:15.409715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1691386
Coefficient of variation (CV)-5.2982113
Kurtosis3.4969835
Mean-0.22066667
Median Absolute Deviation (MAD)0
Skewness0.99962941
Sum-6620
Variance1.3668851
MonotonicityNot monotonic
2025-08-03T15:17:15.530467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5687
 
19.0%
-2 4348
 
14.5%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 4348
 
14.5%
-1 5687
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 69
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5687
 
19.0%

payment_status_may
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2025-08-03T15:17:15.652372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1331874
Coefficient of variation (CV)-4.2569024
Kurtosis3.9897481
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197
Sum-7986
Variance1.2841137
MonotonicityNot monotonic
2025-08-03T15:17:15.774868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4546
 
15.2%
-1 5539
 
18.5%
0 16947
56.5%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 84
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%

payment_status_apr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2025-08-03T15:17:15.894273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1499876
Coefficient of variation (CV)-3.95049
Kurtosis3.4265341
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.94802939
Sum-8733
Variance1.3224715
MonotonicityNot monotonic
2025-08-03T15:17:16.010160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4895
 
16.3%
-1 5740
 
19.1%
0 16286
54.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 49
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%

bill_statement_sep
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.331
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2025-08-03T15:17:16.179734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.861
Coefficient of variation (CV)1.4375453
Kurtosis9.8062893
Mean51223.331
Median Absolute Deviation (MAD)21800.5
Skewness2.663861
Sum1.5366999 × 109
Variance5.42224 × 109
MonotonicityNot monotonic
2025-08-03T15:17:16.351025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2008
 
6.7%
390 244
 
0.8%
780 76
 
0.3%
326 72
 
0.2%
316 63
 
0.2%
2500 59
 
0.2%
396 49
 
0.2%
2400 39
 
0.1%
416 29
 
0.1%
500 25
 
0.1%
Other values (22713) 27336
91.1%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%

bill_statement_aug
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.075
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2025-08-03T15:17:16.535773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.769
Coefficient of variation (CV)1.4472368
Kurtosis10.302946
Mean49179.075
Median Absolute Deviation (MAD)20810
Skewness2.7052209
Sum1.4753723 × 109
Variance5.0657054 × 109
MonotonicityNot monotonic
2025-08-03T15:17:16.712781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2506
 
8.4%
390 231
 
0.8%
326 75
 
0.2%
780 75
 
0.2%
316 72
 
0.2%
396 51
 
0.2%
2500 51
 
0.2%
2400 42
 
0.1%
-200 29
 
0.1%
416 28
 
0.1%
Other values (22336) 26840
89.5%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%

bill_statement_jul
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.155
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2025-08-03T15:17:16.898191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.387
Coefficient of variation (CV)1.475106
Kurtosis19.783255
Mean47013.155
Median Absolute Deviation (MAD)19708.5
Skewness3.08783
Sum1.4103946 × 109
Variance4.8093375 × 109
MonotonicityNot monotonic
2025-08-03T15:17:17.079717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2870
 
9.6%
390 275
 
0.9%
780 74
 
0.2%
326 63
 
0.2%
316 62
 
0.2%
396 48
 
0.2%
2500 40
 
0.1%
2400 39
 
0.1%
416 29
 
0.1%
200 27
 
0.1%
Other values (22016) 26473
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-25443 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%

bill_statement_jun
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.949
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2025-08-03T15:17:17.249052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.856
Coefficient of variation (CV)1.4870197
Kurtosis11.309325
Mean43262.949
Median Absolute Deviation (MAD)18656
Skewness2.8219653
Sum1.2978885 × 109
Variance4.1387164 × 109
MonotonicityNot monotonic
2025-08-03T15:17:17.431387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3195
 
10.7%
390 246
 
0.8%
780 101
 
0.3%
316 68
 
0.2%
326 62
 
0.2%
396 44
 
0.1%
2400 39
 
0.1%
150 39
 
0.1%
2500 34
 
0.1%
416 33
 
0.1%
Other values (21538) 26139
87.1%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-65167 1
< 0.1%
-50616 1
< 0.1%
-46627 1
< 0.1%
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
706864 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
542653 1
< 0.1%

bill_statement_may
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.401
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2025-08-03T15:17:17.598608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.156
Coefficient of variation (CV)1.5081876
Kurtosis12.305881
Mean40311.401
Median Absolute Deviation (MAD)17688.5
Skewness2.8763799
Sum1.209342 × 109
Variance3.6962941 × 109
MonotonicityNot monotonic
2025-08-03T15:17:17.765829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3506
 
11.7%
390 235
 
0.8%
780 94
 
0.3%
316 79
 
0.3%
326 62
 
0.2%
150 58
 
0.2%
396 47
 
0.2%
2400 39
 
0.1%
2500 37
 
0.1%
416 36
 
0.1%
Other values (21000) 25807
86.0%
ValueCountFrequency (%)
-81334 1
< 0.1%
-61372 1
< 0.1%
-53007 1
< 0.1%
-46627 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-28335 1
< 0.1%
-23003 1
< 0.1%
-20753 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%

bill_statement_apr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.76
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2025-08-03T15:17:17.916356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.108
Coefficient of variation (CV)1.5320661
Kurtosis12.270705
Mean38871.76
Median Absolute Deviation (MAD)16755
Skewness2.8466446
Sum1.1661528 × 109
Variance3.5466917 × 109
MonotonicityNot monotonic
2025-08-03T15:17:18.116899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4020
 
13.4%
390 207
 
0.7%
780 86
 
0.3%
150 78
 
0.3%
316 77
 
0.3%
326 56
 
0.2%
396 45
 
0.1%
416 36
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
Other values (20594) 25330
84.4%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-46627 1
< 0.1%
-45734 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
499100 1
< 0.1%

previous_payment_sep
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:18.298053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28
Coefficient of variation (CV)2.9245246
Kurtosis415.25474
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.668364
Sum1.6990742 × 108
Variance2.7434226 × 108
MonotonicityNot monotonic
2025-08-03T15:17:18.452811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5249
 
17.5%
2000 1363
 
4.5%
3000 891
 
3.0%
5000 698
 
2.3%
1500 507
 
1.7%
4000 426
 
1.4%
10000 401
 
1.3%
1000 365
 
1.2%
2500 298
 
1.0%
6000 294
 
1.0%
Other values (7933) 19508
65.0%
ValueCountFrequency (%)
0 5249
17.5%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 15
 
0.1%
4 18
 
0.1%
5 12
 
< 0.1%
6 15
 
0.1%
7 9
 
< 0.1%
8 8
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300039 1
< 0.1%

previous_payment_aug
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:18.619714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.87
Coefficient of variation (CV)3.8912741
Kurtosis1641.6319
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.453817
Sum1.776349 × 108
Variance5.3088171 × 108
MonotonicityNot monotonic
2025-08-03T15:17:18.818854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5396
 
18.0%
2000 1290
 
4.3%
3000 857
 
2.9%
5000 717
 
2.4%
1000 594
 
2.0%
1500 521
 
1.7%
4000 410
 
1.4%
10000 318
 
1.1%
6000 283
 
0.9%
2500 251
 
0.8%
Other values (7889) 19363
64.5%
ValueCountFrequency (%)
0 5396
18.0%
1 15
 
0.1%
2 20
 
0.1%
3 18
 
0.1%
4 11
 
< 0.1%
5 25
 
0.1%
6 8
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%

previous_payment_jul
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:19.005129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.961
Coefficient of variation (CV)3.3693139
Kurtosis564.31123
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.216635
Sum1.5677044 × 108
Variance3.1000509 × 108
MonotonicityNot monotonic
2025-08-03T15:17:19.188675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5968
 
19.9%
2000 1285
 
4.3%
1000 1103
 
3.7%
3000 870
 
2.9%
5000 721
 
2.4%
1500 490
 
1.6%
4000 381
 
1.3%
10000 312
 
1.0%
1200 243
 
0.8%
6000 241
 
0.8%
Other values (7508) 18386
61.3%
ValueCountFrequency (%)
0 5968
19.9%
1 13
 
< 0.1%
2 19
 
0.1%
3 14
 
< 0.1%
4 15
 
0.1%
5 18
 
0.1%
6 14
 
< 0.1%
7 18
 
0.1%
8 10
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%

previous_payment_jun
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.0769
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:19.355828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.16
Coefficient of variation (CV)3.246148
Kurtosis277.33377
Mean4826.0769
Median Absolute Deviation (MAD)1500
Skewness12.904985
Sum1.4478231 × 108
Variance2.4542856 × 108
MonotonicityNot monotonic
2025-08-03T15:17:19.539160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6408
 
21.4%
1000 1394
 
4.6%
2000 1214
 
4.0%
3000 887
 
3.0%
5000 810
 
2.7%
1500 441
 
1.5%
4000 402
 
1.3%
10000 341
 
1.1%
2500 259
 
0.9%
500 258
 
0.9%
Other values (6927) 17586
58.6%
ValueCountFrequency (%)
0 6408
21.4%
1 22
 
0.1%
2 22
 
0.1%
3 13
 
< 0.1%
4 20
 
0.1%
5 12
 
< 0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
432130 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%

previous_payment_may
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.3876
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:19.726795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.306
Coefficient of variation (CV)3.1833865
Kurtosis180.06394
Mean4799.3876
Median Absolute Deviation (MAD)1500
Skewness11.127417
Sum1.4398163 × 108
Variance2.3342662 × 108
MonotonicityNot monotonic
2025-08-03T15:17:19.889673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6703
 
22.3%
1000 1340
 
4.5%
2000 1323
 
4.4%
3000 947
 
3.2%
5000 814
 
2.7%
1500 426
 
1.4%
4000 401
 
1.3%
10000 343
 
1.1%
500 250
 
0.8%
6000 247
 
0.8%
Other values (6887) 17206
57.4%
ValueCountFrequency (%)
0 6703
22.3%
1 21
 
0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 12
 
< 0.1%
5 9
 
< 0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%

previous_payment_apr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.5026
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2025-08-03T15:17:20.056901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.466
Coefficient of variation (CV)3.4085815
Kurtosis167.16143
Mean5215.5026
Median Absolute Deviation (MAD)1500
Skewness10.640727
Sum1.5646508 × 108
Variance3.1603829 × 108
MonotonicityNot monotonic
2025-08-03T15:17:20.226805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7173
23.9%
1000 1299
 
4.3%
2000 1295
 
4.3%
3000 914
 
3.0%
5000 808
 
2.7%
1500 439
 
1.5%
4000 411
 
1.4%
10000 356
 
1.2%
500 247
 
0.8%
6000 220
 
0.7%
Other values (6929) 16838
56.1%
ValueCountFrequency (%)
0 7173
23.9%
1 20
 
0.1%
2 9
 
< 0.1%
3 14
 
< 0.1%
4 12
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30,000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Length

2025-08-03T15:17:20.374140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T15:17:20.493576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Interactions

2025-08-03T15:17:08.270692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:04.375198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:08.127763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.343160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:14.627817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.730367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.944934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.473419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.386563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.377548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.335531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.583676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:39.710919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:43.229867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:46.280759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:49.284167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.484997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:55.543291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:59.137091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:02.030448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:05.106490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:08.403950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:04.945851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:08.300336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.486777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:14.790268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.884104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:21.089129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.615838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.535426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.512862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.515689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.722901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:39.854954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:43.370614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:46.431706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:49.432982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.627533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:55.691158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:59.273020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:02.190528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:05.258875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:08.553306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:05.114900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:08.472036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.630167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:14.993301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:18.018661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:21.227829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.752205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.710118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.640515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.657846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.847589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:40.005128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:43.510303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:46.569798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:49.574026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.772615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:55.867657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:59.411569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:02.313899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:05.390891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:08.696882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:05.275612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:08.639750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.777971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:15.158534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:18.168108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:21.365992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.890857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.860034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.791344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.799190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:37.014112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:40.154236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:43.668203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:46.717198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:49.725361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.915605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:56.062377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:59.549665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:02.468080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:05.527540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:08.837278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:05.404303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:08.783279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.915363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:15.293570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:18.329361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:21.503818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:25.024455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.994411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.931682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.939260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2025-08-03T15:17:03.907732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:06.946090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:10.288460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.000097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:10.223226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:13.421579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:16.747281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:19.863610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:23.503520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:26.410847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:29.386436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:32.316701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:35.511748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:38.676909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:42.183265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:45.292383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:48.219920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:51.408329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:54.490991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:57.738649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.090013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.078077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:07.113326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:10.438485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.182478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:10.426823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:13.577262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:16.894151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.033230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:23.651045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:26.561031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:29.547746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:32.463799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:35.658468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:38.828195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:42.329938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:45.443718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:48.404370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:51.565939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:54.680071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:58.252889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.233636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.241722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:07.268959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:10.569789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.313250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:10.571604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:13.704794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.031297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.155255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:23.780832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:26.687951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:29.675692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:32.581218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:35.841883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:38.954985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:42.479660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:45.557403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:48.548848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:51.734241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:54.821984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:58.384540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.361875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.379451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:07.433299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:10.708687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.501478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:10.740788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:13.953495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.196749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.341899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:23.917914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:26.828476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:29.827455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:32.732465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.016631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:39.109154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:42.637793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:45.728794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:48.706010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:51.895537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:54.972486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:58.552555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.514014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.544697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:07.601211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:10.850943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.634060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:10.871227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:14.088401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.313192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.475161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.052975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:26.962347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:29.959320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:32.877204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.149263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:39.257284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:42.783762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:45.861814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:48.840111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.036091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:55.106438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:58.690504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.640341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.671826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:07.785777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:10.995896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.802287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.029563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:14.310824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.464452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.626806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.206737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.116757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.105683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.019117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.295661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:39.424047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:42.941887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:45.993904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:48.990770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.190586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:55.256999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:58.846231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.780765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.827732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:07.953175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:11.129933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:07.934390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:11.163467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:14.452694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:17.606021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:20.760987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:24.338384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:27.248502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:30.222431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:33.172045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:36.434954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:39.558777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:43.083997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:46.126173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:49.152036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:52.330723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:55.389224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:16:58.989689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:01.895739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:04.977700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-08-03T15:17:08.103105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2025-08-03T15:17:20.641679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idlimit_balagepayment_status_seppayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_aprbill_statement_sepbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_aprprevious_payment_sepprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_aprsexeducationmarriagedefault_payment_next_month
id1.0000.0310.025-0.025-0.005-0.009-0.004-0.016-0.0060.0110.0100.0160.0330.0150.0200.0120.0510.0940.0220.0120.0380.1790.0480.0250.038
limit_bal0.0311.0000.186-0.296-0.343-0.332-0.309-0.285-0.2640.0540.0490.0610.0730.0810.0880.2720.2780.2840.2830.2940.3170.0730.1590.0780.157
age0.0250.1861.000-0.064-0.083-0.083-0.080-0.083-0.0760.0010.0020.002-0.003-0.0000.0000.0340.0440.0330.0410.0380.0390.0910.1570.3500.048
payment_status_sep-0.025-0.296-0.0641.0000.6270.5480.5160.4860.4640.3150.3300.3140.3070.2990.289-0.098-0.064-0.054-0.034-0.026-0.0450.0660.1140.0390.423
payment_status_aug-0.005-0.343-0.0830.6271.0000.7990.7130.6740.6350.5710.5510.5190.4980.4780.4590.0200.0840.0870.0950.0990.0820.0750.1230.0400.340
payment_status_jul-0.009-0.332-0.0830.5480.7991.0000.8010.7180.6710.5240.5890.5570.5310.5070.4850.2160.0370.1030.1190.1240.0980.0710.1190.0370.295
payment_status_jun-0.004-0.309-0.0800.5160.7130.8011.0000.8220.7320.5120.5580.6190.5930.5610.5340.1850.2460.0690.1440.1620.1430.0640.1100.0410.279
payment_status_may-0.016-0.285-0.0830.4860.6740.7180.8221.0000.8210.4990.5380.5870.6500.6180.5790.1750.2220.2600.1070.1850.1720.0560.1000.0400.270
payment_status_apr-0.006-0.264-0.0760.4640.6350.6710.7320.8211.0000.4880.5240.5610.6060.6680.6300.1780.2000.2380.2840.1410.1980.0460.0970.0350.250
bill_statement_sep0.0110.0540.0010.3150.5710.5240.5120.4990.4881.0000.9110.8580.8070.7690.7340.5020.4720.4410.4420.4250.4100.0260.0410.0220.031
bill_statement_aug0.0100.0490.0020.3300.5510.5890.5580.5380.5240.9111.0000.9080.8480.8030.7650.6360.4980.4680.4610.4490.4290.0330.0580.0180.031
bill_statement_jul0.0160.0610.0020.3140.5190.5570.6190.5870.5610.8580.9081.0000.9040.8490.8040.5500.6380.4920.4890.4770.4580.0180.0590.0160.000
bill_statement_jun0.0330.073-0.0030.3070.4980.5310.5930.6500.6060.8070.8480.9041.0000.9030.8480.5120.5550.6340.5070.5040.4810.0260.0360.0210.019
bill_statement_may0.0150.081-0.0000.2990.4780.5070.5610.6180.6680.7690.8030.8490.9031.0000.9020.4830.5150.5490.6470.5250.5090.0210.0570.0210.017
bill_statement_apr0.0200.0880.0000.2890.4590.4850.5340.5790.6300.7340.7650.8040.8480.9021.0000.4560.4870.5190.5700.6660.5290.0260.0280.0220.022
previous_payment_sep0.0120.2720.034-0.0980.0200.2160.1850.1750.1780.5020.6360.5500.5120.4830.4561.0000.5120.5190.4860.4680.4550.0000.0000.0340.027
previous_payment_aug0.0510.2780.044-0.0640.0840.0370.2460.2220.2000.4720.4980.6380.5550.5150.4870.5121.0000.5160.5200.4970.4910.0000.0000.0240.013
previous_payment_jul0.0940.2840.033-0.0540.0870.1030.0690.2600.2380.4410.4680.4920.6340.5490.5190.5190.5161.0000.5160.5340.5050.0120.0210.0230.024
previous_payment_jun0.0220.2830.041-0.0340.0950.1190.1440.1070.2840.4420.4610.4890.5070.6470.5700.4860.5200.5161.0000.5340.5470.0000.0000.0350.022
previous_payment_may0.0120.2940.038-0.0260.0990.1240.1620.1850.1410.4250.4490.4770.5040.5250.6660.4680.4970.5340.5341.0000.5490.0140.0170.0100.035
previous_payment_apr0.0380.3170.039-0.0450.0820.0980.1430.1720.1980.4100.4290.4580.4810.5090.5290.4550.4910.5050.5470.5491.0000.0120.0290.0020.028
sex0.1790.0730.0910.0660.0750.0710.0640.0560.0460.0260.0330.0180.0260.0210.0260.0000.0000.0120.0000.0140.0121.0000.0260.0300.039
education0.0480.1590.1570.1140.1230.1190.1100.1000.0970.0410.0580.0590.0360.0570.0280.0000.0000.0210.0000.0170.0290.0261.0000.1340.072
marriage0.0250.0780.3500.0390.0400.0370.0410.0400.0350.0220.0180.0160.0210.0210.0220.0340.0240.0230.0350.0100.0020.0300.1341.0000.030
default_payment_next_month0.0380.1570.0480.4230.3400.2950.2790.2700.2500.0310.0310.0000.0190.0170.0220.0270.0130.0240.0220.0350.0280.0390.0720.0301.000

Missing values

2025-08-03T15:17:11.378181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-03T15:17:11.980488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idlimit_balsexeducationmarriageagepayment_status_seppayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_aprbill_statement_sepbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_aprprevious_payment_sepprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_aprdefault_payment_next_month
0120000FemaleUniversityMarried2422-1-1-2-239133102689000068900001
12120000FemaleUniversitySingle26-1200022682172526823272345532610100010001000020001
2390000FemaleUniversitySingle340000002923914027135591433114948155491518150010001000100050000
3450000FemaleUniversityMarried370000004699048233492912831428959295472000201912001100106910000
4550000MaleUniversityMarried57-10-100086175670358352094019146191312000366811000090006896790
5650000MaleGraduate schoolSingle3700000064400570695760819394196192002425001815657100010008000
67500000MaleGraduate schoolSingle290000003679654120234450075426534830034739445500040000380002023913750137700
78100000FemaleUniversitySingle230-1-100-111876380601221-1595673806010581168715420
89140000FemaleHigh schoolMarried2800200011285140961210812211117933719332904321000100010000
91020000MaleHigh schoolSingle35-2-2-2-2-1-10000130071391200013007112200
idlimit_balsexeducationmarriageagepayment_status_seppayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_aprbill_statement_sepbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_aprprevious_payment_sepprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_aprdefault_payment_next_month
2999029991140000MaleUniversityMarried4100000013832513714213911013826249675461216000700042281505200020000
2999129992210000MaleUniversityMarried343222222500250025002500250025000000001
299922999310000MaleHigh schoolMarried43000-2-2-288021040000002000000000
2999329994100000MaleGraduate schoolSingle380-1-100030421427102996706266947355004200011178440003000200020000
299942999580000MaleUniversitySingle342222227255777708793847751982607811587000350007000040001
2999529996220000MaleHigh schoolMarried3900000018894819281520836588004312371598085002000050033047500010000
2999629997150000MaleHigh schoolSingle43-1-1-1-100168318283502897951900183735268998129000
299972999830000MaleUniversitySingle37432-10035653356275820878205821935700220004200200031001
299982999980000MaleHigh schoolMarried411-1000-1-16457837976304527741185548944859003409117819265296418041
299993000050000MaleUniversityMarried460000004792948905497643653532428153132078180014301000100010001